Health lending system and method using probabilistic graph models

ABSTRACT

A system and method for health lending using a probabilistic graph model are described. The system and method may generate a health credit score (“HICO”). The HICO is a score that is a risk measure placed on all entities of a healthcare transaction in which a company that owns or operates the system may have an interest or a company that utilizes the HICO score for its risk assessment.

PRIORITY CLAIM/RELATED APPLICATION

This application claims the benefit under 35 USC 119(e) and priorityunder 35 USC 120 to U.S. Provisional Patent Application Ser. No.62/105,503, filed on Jan. 20, 2015, and entitled “Health Lending Systemand Method Using Probabilistic Graph Models”, the entirety of which isincorporated herein by reference.

FIELD

The disclosure relates generally to a health system and method and inparticular to a health lending system and method.

BACKGROUND

The problem of financial credit scoring is a very challenging andimportant financial analysis problem. The main challenge with the creditrisk modeling and assessment is that the current models are riddled withuncertainty. The estimation of the probability of default (insolvency),modeling correlation structure for a group of connected borrowers andestimation of amount of correlation are the most important sources ofuncertainty that can severely impair the quality of credit risk models.

Many techniques have already been proposed to tackle this problem,ranging from statistical classifiers to decision trees, nearest-neighbormethods and neural networks. Although the latter are powerful patternrecognition techniques, their use for practical problem solving (andcredit scoring) is rather limited due to their intrinsic opaque, blackbox nature. The best known method in the industry is the Fair IsaacCorporation (FICO®) score. The FICO Score® is calculated from severaldifferent pieces of credit data in a credit report of a user. The datamay be grouped into five categories as shown in FIG. 1. The percentagesin the chart in FIG. 1 reflect how important each of the categories isin determining how the FICO score of each user is calculated.

The FICO Score considers both positive and negative information in acredit report. For example, late payments will lower your FICO Score,but establishing or re-establishing a good track record of makingpayments on time will raise your score. There are several creditreporting companies that work in conjunction with each other, such asEquifax and Transunion to name but a few organizations. However thesecompanies are not able to properly align themselves to a risk managementmodel that matches health based loans. Specifically they do not know thedemographics, the place of living, the occupation, the length ofemployment and dependents of the person. Further in the case of healthbased services and loans, the risk are increased due to the unhealthynature of the consumer who is getting the health service and must payback the loan.

The recent upsurge in health care costs has seen an increased interestin lending to consumers for health services. It is desirable to providea system and method for determining a health credit score that modelsthe risk of a health care loan to a consumer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a known FICO score;

FIG. 2 illustrates a health services system that may incorporate ahealth lending system;

FIG. 3 illustrates more details of the health lending system;

FIG. 4 illustrates a method for predicting risk;

FIG. 5 illustrates an example of a provider Bayes risk network;

FIG. 6 illustrates an example of a method for a provider Bayes modelgeneration;

FIG. 7 illustrates an example of a method for consumer risk modeling;

FIG. 8 illustrates an example of the output of the BeliefNetwork graphinference model;

FIG. 9 illustrates an example of a PDHICO Provider/Payer example inputdata for the risk assessment;

FIG. 10 illustrates an example of a risk graph for the PDHICOProvider/Payer example data in FIG. 9;

FIG. 11 illustrates an example of the output of the system for thePDHICO Provider/Payer example data in FIG. 9;

FIG. 12 illustrates an example of a PDHICO Consumer example input datafor the risk assessment;

FIG. 13 illustrates an example of a risk graph for the PDHICO consumerexample data in FIG. 12; and

FIG. 14 illustrates an example of the output of the system for thePDHICO consumer example data in FIG. 12.

DETAILED DESCRIPTION OF ONE OR MORE EMBODIMENTS

The disclosure is particularly applicable to a cloud based system andmethod for health lending using a health credit (“HICO”) score that maybe generated from a probabilistic graph model and it is in this contextthat the disclosure will be described. It will be appreciated, however,that the system and method has greater utility since it may use othermodels in order to generate the HICO and may be implemented in differentmanners that those described below that would be within the scope of thedisclosure. Although the health lending system is shown integrated witha health services system in the figures and descriptions below, thehealth lending system also may be a standalone system or integrated intoother systems.

The health credit score (“HICO”) generated by the system and methoddescribed below has several factors that differentiate the HICO from thecredit score described above. The HICO is a score that is a risk measureplaced on all entities of a healthcare transaction in which a companythat owns or operates the system may have an interest or a company thatutilizes the HICO score for its risk assessment. Each entity may be ahealth service consumer, a health service provider or a health servicepayer. However, the system and method described herein may be used toperform a risk measure on various other entities that are part of thehealth services industry or space. For purposes of illustration below,the description below may consider the entity types described above andthen consider the specific risk each entity poses as well as the dataand models that may be used to generate the HICO.

FIG. 2 illustrates a health services system 100 that may incorporate ahealth lending system. The health services system 100 may have one ormore computing devices 102 that connect over a communication path 106 toa backend system 108. Each computing device 102, such as computingdevices 102 a, 102 b, 102 n as shown in FIG. 1, may be a processor baseddevice with memory, persistent storage, wired or wireless communicationcircuits and a display that allows each computing device to connect toand couple over the communication path 106 to a backend system 108. Forexample, each computing device may be a smartphone device, such as anApple Computer product, Android OS based product, etc., a tabletcomputer, a personal computer, a terminal device, a laptop computer andthe like. In one embodiment shown in FIG. 2, each computing device 102may store an application 104 in memory and then execute that applicationusing the processor of the computing device to interface with thebackend system. For example, the application may be a typical browserapplication or may be a mobile application. The communication path 106may be a wired or wireless communication path that uses a secureprotocol or an unsecure protocol. For example, the communication path106 may be the Internet, Ethernet, a wireless data network, a cellulardigital data network, a WiFi network and the like.

The backend system 108 may have a health marketplace engine 110 and ahealth lending system 113 that may be coupled together. Each of thesecomponents of the backend system may be implemented using one or morecomputing resources, such as one or more server computers, one or morecloud computing resources and the like. In one embodiment, the healthmarketplace engine 110 and the health lending system 113 may each beimplemented in software in which each has a plurality of lines ofcomputer code that are executed by a processor of the one or morecomputing resources of the backend system. Thus, in that embodiment, theprocessor of the one or more computing resources of the backend systemis configured to perform the operations and functions of the marketplaceand health lending system as described below. In other embodiments, eachof the health marketplace engine 110 and the health lending system 113may be implemented in hardware such as a programmed logic device, aprogrammed processor or microcontroller and the like. The backend system108 may be coupled to a store 114 that stores the various data andsoftware modules that make up the healthcare system and the healthlending system. The store 114 may be implemented as a hardware databasesystem, a software database system or any other storage system.

The health marketplace engine 110 may allow practitioners that havejoined the healthcare social community to reach potential clients inways unimaginable even a few years ago. In addition to givingpractitioners a social portal with which to communicate and marketthemselves with consumers, the marketplace gives each healthcarepractitioner the ability to offer their services in an environment thatis familiar to users of Groupon, Living Social, or other socialmarketplaces.

The health lending system, as described below, is a system that receivesdata about an entity involved in a health services transaction and thengenerates a risk measure for the entity involved in the health servicestransaction. In one embodiment, the risk measure may be in the form of ahealth credit score (“HICO”) that is a risk measure placed on allentities of a healthcare or health services transaction for a companythat owns or operates the system may have an interest or a company thatutilizes the HICO score for its risk assessment. Each entity may be ahealth service consumer, a health service provider or a health servicepayer. However, the system and method described herein may be used toperform a risk measure on various other entities that are part of thehealth services industry or space. In one embodiment, the risk measuremay be based on a probabilistic graph model as described below.

FIG. 3 illustrates more details of the health lending system 113. Thehealth lending system 113 may have an input processor 300 that receiveshealth data input information about an entity and processes it for useby the health lending system 113 such as by, for example, performing anETL process. The output from the input processor may be fed into a graphmodel engine 302 that generates a probabilistic graph model based on thehealth data information for the entity and the output from the graphmodel engine 302 may be fed into a health credit score generator 304that generates a health credit score for the entity based on theprobabilistic graph model.

Now, the health data input information for the health lending system isdescribed in more detail. The health data input information may includeone or more vectors of data that may be generated by an “ExtractTransform and Load Process (ETL)” and accessible via the health systemusing Application Programmer Interfaces (APIs). An example of the dataentities that may be used for the Probabilistic Graph structure(s) andthe graph model engine 302 appear below. However, additional or otherdata vectors may be used and those other data vectors may be from otherETL processes. Thus, the example data vectors below are merelyillustrative and the disclosure is directed to various different typesof data vectors that may be used by the graph model engine.

Consumer Entity

In some embodiments, the health services system may be a hybridbank/insurer in which the consumers present two possible risk profiles.Specifically, the health services system may provide consumers withtraditional health insurance as well as lend them money to pay forhealth services.

Health Services Lending

In the context of lending money for health services (including healthcare procedures), the main risk is that the consumer defaults on theloan, thus the main metrics for determining default risk will probablybe measures like the known FICO credit score, credit history, availableassets (liquid and non-liquid), willingness and/or ability to back theloan by collateral, etc. The overall current FICO model forcreditworthiness breaks down as follows:

-   -   35% Payment History    -   30% Debt Burden    -   15% Length of credit history    -   10% Types of credit used    -   10% Recent searches for credit

Health Insurance

The setting of risk adjustment in terms of health insurance premiums isan extremely complicated and regulated (especially under the ACA)process. The system may also collect data on plan members that may notbe a part of the usual risk models in exchange for lower premiums. Thesedatasets could possibly include data from personal wearable devices(Fitbit, Jawbone, smartphone apps) that track lifestyle and biologicalmeasurements, as well as past medical records.

For health insurance lending, the health system may have the followinginformation about each person who wants health insurance.

-   -   Basic demographics (i.e. location, gender, DOB)

Insurance Information

-   -   Member ID    -   Group ID    -   Total deductible    -   Remaining deductible    -   Dependents

Marketplace Interactions

-   -   Specialties Searched    -   Conditions Searched    -   Purchases made    -   Log in frequency    -   Providers rated/reviewed

Self Reported Health Statistics Via Wearable APIs

-   -   BMI    -   Smoker status    -   Activity Level    -   Wellness program memberships

Financial Information

-   -   FICO Credit Score    -   Credit reports    -   Assets/Debts    -   Lending data from Lending Club

Social Network Interaction Data

-   -   twitter, facebook, linked

Wearable API Data

-   -   Measured activity level    -   Sleep cycles

Provider Entities

In addition to processing insurance claims for providers, the healthlending system may also immediately pay providers for their servicesinstead of them having to wait for the payer to process a claim and thenthe health lending system is reimbursed from the payer. The risk inimmediately paying providers for services rendered is that either theprice we pay the provider is more than the price that will be reimbursedby the payer or worse that the service is not covered at all by theconsumer's insurance. An analysis of current trends shows that onaverage for both private insurance and Medicare, providers submit claimsfor higher than the allowed price with the mentality for providers tobill at the highest price and hope for the best. The application of thecurrent system accurately predicts what a payer is actually going to payfor a given claim so that we can minimize the number of transactionswhere the amount we pay the provider is higher than the amountreimbursed by the payer.

The health lending system has various information about each providerincluding:

Provider Demographics (Age, Gender, Location)

Medical Education

-   -   Where and when they went to medical school    -   Where and when they did their residencies/fellowships    -   Specialties    -   Credentials    -   Hospital affiliations

Pricing

-   -   Submitted and paid prices from medicare    -   Cash prices    -   Services listed on the marketplace    -   Responses to requests for quote

Ratings, Reviews, Recognitions

-   -   Reviews and ratings from marketplace    -   Malpractice Sanctions from state licensure bodies we receive        from the American Medical Association    -   Ratings and Reviews

Claims Statistics

-   -   Number of claims submitted, submitted price, reimbursed price        per procedure    -   Number of rejected claims per procedure

Payer Entities (Aka Insurance Carriers, Trading Partners)

In the situation where the health lending system immediately remitpayment to a provider for a service, the health lending system thensubmits the claim to the payer to collect payment. Like with theproviders, the main risk is in the amount reimbursed from the payer fora service and the length of time it takes for the reimbursement.

The health lending system has various information about each payerincluding (from the processing of X12 transactions):

-   -   Payment Statistics: How much does payer X pay on average for        procedure Y.    -   Statistics about time taken to process claims (i.e. average        processing time, average time per procedure, etc.)    -   Statistics about rejected claims. Analysis of claims in general        and claims segmented by payer will probably allow us to build        predictive models for determining the probability that a claim        will be rejected.

Thus, for each of the above entities, the health lending system mayassess or predict the risk associated with the transaction with each ofthe above entities. The risk prediction may be a mixture of the threescores based on a probability model.

FIG. 4 illustrates a method 400 for predicting risk for each of thedifferent entities that may be performed, in one embodiment, by thehealth lending system 113 shown in FIGS. 2-3. The method illustrated inFIG. 4 also may be performed by software that is a plurality of lines ofcomputer code that may be executed by a processor of the one or morecomputing resources so that the processor is configured to perform theoperations and functions of the risk prediction as described below. Inother embodiments, the method may be performed using hardware such as aprogrammed logic device, a programmed processor, a microcontroller, anapplication specific integrated circuit and the like.

As shown in FIG. 4, the method may receive, for a payor, information forthe risk prediction based on processing claims and benefits (examples ofwhich are set forth above) via ETL (402). When the health lendingcomponent 113 in FIG. 3 is performing the method, the input processor300 may perform this processing. The information about the payor may beused in the method to create a payor Bayes graph (404). When the healthlending component 113 in FIG. 3 is performing the method, the graphmodel engine 302 may create this Bayes graph. The Bayes graph for thepayor may also exchange data with a generated provider Bayes graph.

For a provider entity, the method may process provider information(examples of which are set forth above) via an API (406). When thehealth lending component 113 in FIG. 3 is performing the method, theinput processor 300 may perform this processing. The information aboutthe provider may be used in the method to create a provider Bayes graph(408). When the health lending component 113 in FIG. 3 is performing themethod, the graph model engine 302 may create this Bayes graph. Theprovider Bayes graph may also exchange data with the payer Bayes Graph.

For a consumer entity, the method may process consumer information(examples of which are set forth above) (410). When the health lendingcomponent 113 in FIG. 3 is performing the method, the input processor300 may perform this processing. The information about the consumer (andthe claims and benefits information from the ETL) may be used in themethod to create a consumer Bayes graph (412). When the health lendingcomponent 113 in FIG. 3 is performing the method, the graph model engine302 may create this Bayes graph.

As shown, one or more of the created Bayes graphs may be used in themethod to create a health credit risk score ranking (414). When thehealth lending component 113 in FIG. 3 is performing the method, thehealth credit score generator 304 may generate the score(s). An exampleof this method is described below starting at FIG. 9. Thus, the methodgenerates one or more health credit risk score(s) that may be used bythe health lending system to assess the risk of the health servicesrelated lending described above.

In the above method, a Bayesian network classifiers is used thatprovides the capacity of giving a clear insight into the structuralrelationships in the domain under investigation. In addition, as oflate, Bayesian network classifiers have had success for financial creditscoring. In the method, a BeliefNetwork or a network of Bayes Nets whichis also called a Probabilistic Graph Model (PGM) may be used.

The method uses probabilistic graphs for modeling and assessment ofcredit concentration risk based on health and medical behaviors as afunction of the risk. The destructive power of credit concentrationsessentially depends on the amount of correlation among borrowers.However, borrower company's correlation and concentration of credit riskexposures have been difficult for the banking industry to measure in anobjective way as they are riddled with uncertainty. As a result, banksdo not manage to make a quantitative link to the correlation drivingrisks and fail to prevent concentrations from accumulating. However,since the health lending system and method has the ability to exacthealth and medical behaviors tied to the consumer, the method createsBeliefNetworks that provide an attractive solution to the usual creditscoring problems, specifically within the health domain to show how toapply them in representing, quantifying and managing the uncertainknowledge in concentration of “health credits risk exposures”.

The method may use a stepwise Belief network model building scheme andthe method may incorporate prior beliefs regarding the risk exposure ofa group of related behavior such as office revisits and outcomes andthen update these beliefs through the whole model with the newinformation as it is learned. The method may use a specific graphstructure, BeliefNetwork network, and the model provides betterunderstanding of the concentration risk accumulating due to strongdirect or indirect business links between borrowers and lenders as afunction of the health data that is used. The method also may use threemodel BeliefNetworks and the mutual information and construct aBeliefNetwork that is a reliable model that can be implemented toidentify and control threat from concentration of credit exposures.

A Bayesian network (BN) represents a joint probability distribution overa set of discrete attributes. It is to be considered as a probabilisticwhite-box model consisting of a qualitative part specifying theconditional (inter)dependencies between the attributes and aquantitative part specifying the conditional probabilities of the dataset attributes. Formally, a Bayesian network consists of two parts,B=<G, θ>, wherein a directed acyclic graph G consisting of nodes andarcs and one or more conditional probability tables θ=(θ×1, . . . θ×n).The nodes are the attribute X_(i) . . . X_(n) whereas the arcs indicatedirect dependencies. The Bayesian network is essentially a statisticalmodel that makes it feasible to compute the (joint) posteriorprobability distribution of any subset of unobserved stochasticvariables, given that the variables in the complementary subset areobserved.

The graph G then encodes the independence relationships of the domain.The network B represents the following joint probability distribution:

P _(B)(X ₁ . . . X _(n))=Π_(i=1) ^(n) Pb(Xi|ΠXi)=Π_(i=1) ^(n)θXi|ΠXi  (Equation 1)

This yields a Bayesian probabilistic graph structure that is familiar tothose trained in the art.

Given this basic functionality, a baseline for discreet tractablevariables is created where inference is exact. In our cases of the threeprobabilistic graph models (as shown in FIG. 4), we have three tractableinference models.

In order to improve the performance of the models and graphs, forcontinuous variables such as pricing information, the method may utilizeMarkov chain Monte Carlo (MCMC hereafter) which samples directly fromthe posterior distributions. The method may also use the well knownMetropolis-Hastings algorithm for Markov Chains. The Metropolis-Hastingsalgorithm was first adapted for structural learning of Bayesian andMarkov Networks by Madigan and York. In the method; metropolis—Hastingsand other MCMC algorithms are generally used for sampling frommulti-dimensional distributions, especially when the number ofdimensions is high. In our case of the Health Credit Score Modelsdescribed above, the models are considered high dimensionality.

The BeliefNetworks building process for the provider network model andbuild process may use the following code example:

provider_bayes_net = build_bayes_belief_net twork(prob_procedures_performed_by_specialty_count,prob_procedures_performed_count, prob_provider_procedure_experience,prob_provider_age, prob_provider_experience,prob_provider_revoked_license, prob_provider_ability,prob_claim_line_edited_to_zero, prob_good_payer_price_data,prob_pd_payment_payer_paid_difference, prob_loss, domains=dict(procedures_performed_by_specialty_count=variable_range_bins,procedures_performed_count=variable_range_bins,provider_procedure_experience=variable_range_bins,provider_age=[‘veryold’, ‘old’, ‘middle_age’, ‘young’],provider_experience=variable_range_bins,provider_revoked_license=boolean_variable_vals,provider_ability=variable_range_bins,claim_line_edited_to_zero=boolean_variable_vals,good_payer_price_data=boolean_variable_vals,pd_payment_payer_paid_difference=variable_range_bins,loss=boolean_variable_vals )) return provider_bayes_net

An example of the provider payer risk Bayes network in shown in FIG. 5.As shown in FIG. 5, the various information for a provider riskassessment are shown. FIG. 6 shows an example of a method 600 for aprovider Bayes model generation starting with a procedure bundle foreach health procedure wherein each procedure may be a single CPT code. Atypical medical/health procedure usually consists of more than onebilling procedure code (CPT code). The method calculates a PDHICO scorefor each line item (CPT code) and then combines them into an overallprocedure bundle score. The method may use the patient data, payer dataand/or provider data to generate a procedure risk assessment 602 foreach of the procedures. Based on the risk assessment of each of theprocedures, the method may determine a procedure bundle risk assessmentscore 604. Based on the procedure bundle risk assessment score, thehealth lending system may determine (606) whether or not to immediatelyreimburse the provider that may depend in part on a lender risktoleration.

FIG. 7 illustrates an example of a method 700 for consumer risk modelingin which information about the consumer (702) is used. When the healthlending component 113 in FIG. 3 is performing the method, the graphmodel engine 302 may perform the method. The information about theconsumer may include a FICO credit score, financial data, wearabledevice data, health record data and insurance plan data. The informationabout the consumer may be used to make a consumer lending assessment(704). The consumer lending assessment 704 may be combined with dataabout a bank/lender (706) to determine (708) whether to lend money tothe patient for the procedure. The determination about lending the moneymay also depend on the lender risk tolerance (710).

FIG. 8 illustrates an example of the output of the BeliefNetwork graphinference model that shows the calculated probabilities that ultimatelyresult in a probability of loss if the lending is made to a particularconsumer.

FIG. 9 illustrates an example of a PDHICO Provider/Payer example inputdata for the risk assessment. Thus, FIG. 9 shows exemplary input dataincluding a reimbursed price, reimbursed price stats and proceduresperformed by the provider. FIG. 10 illustrates an example of a riskgraph for the PDHICO Provider/Payer example data in FIG. 9 generated bythe system and method described above. As shown in FIG. 10, variousvalues for the example data in FIG. 9 are generated and placed into therisk graph. For example, the probability of loss, P(loss), is calculatedas 0.701 as shown in FIG. 10. The probability of loss may be calculatedusing the methodology described above and using equation (1). The methodmay use the probabilities encoded in the graphical model to calculatethe conditional probabilityP(loss=Truelprocedures_performed_by_specialty_count=“high”,payment_average_payer_paid_amount_difference=“medium”). This is wherethe representational efficiency of the graphical model comes into playas the conditional probability table for the resulting equation has morethan 177, 147 terms. FIG. 11 illustrates an example of the output of thesystem for the PDHICO Provider/Payer example data in FIG. 9 includingthe scores generated by the system. In one embodiment, the risk scoremay be calculated as (1−P(loss)*100). Then, from the example data inFIG. 9, the system generates a risk score of 30 calculated as(1−0.7)*100) from the value calculated in FIG. 9.

FIG. 12 illustrates an example of a PDHICO Consumer example input datafor the risk assessment in which various particulars about the consumerincluding health details and financial details are stored. In oneembodiment, the consumer risk may be calculated as 0.63 based on theconsumer input data. FIG. 13 illustrates an example of a risk graph forthe PDHICO consumer example data in FIG. 12 generated by the system andmethod described above. As shown in FIG. 13, various values for theexample data in FIG. 12 are generated and placed into the risk graph.For example, the probability of loss, P(loss), is calculated as 0.37 asshown in FIG. 13. The probability of loss may be calculated using themethodology described above and using equation (1). The method may usethe probabilities encoded in the graphical model to calculate theconditional probability P(loss=Truelconsumer_bmi=“overweight”,consumer_gender=“male”, smoker=“false”, consumer_age=“40-69”,debt_ratio=“30-35”, fico=“700-749”) This is where the representationalefficiency of the graphical model comes into play as the conditionalprobability table for the resulting equation has more than 43,046,721terms. FIG. 14 illustrates an example of the output of the system forthe PDHICO consumer example data in FIG. 12 including the scoresgenerated by the system. In one embodiment, the risk score may becalculated as (1−P(loss)*100). Then, from the example data in FIG. 12,the system generates a risk score of 63 calculated as (1−0.37)*100) fromthe value calculated in FIG. 12.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the disclosure to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the disclosure and its practical applications, to therebyenable others skilled in the art to best utilize the disclosure andvarious embodiments with various modifications as are suited to theparticular use contemplated.

The system and method disclosed herein may be implemented via one ormore components, systems, servers, appliances, other subcomponents, ordistributed between such elements. When implemented as a system, suchsystems may include an/or involve, inter alia, components such assoftware modules, general-purpose CPU, RAM, etc. found ingeneral-purpose computers. In implementations where the innovationsreside on a server, such a server may include or involve components suchas CPU, RAM, etc., such as those found in general-purpose computers.

Additionally, the system and method herein may be achieved viaimplementations with disparate or entirely different software, hardwareand/or firmware components, beyond that set forth above. With regard tosuch other components (e.g., software, processing components, etc.)and/or computer-readable media associated with or embodying the presentinventions, for example, aspects of the innovations herein may beimplemented consistent with numerous general purpose or special purposecomputing systems or configurations. Various exemplary computingsystems, environments, and/or configurations that may be suitable foruse with the innovations herein may include, but are not limited to:software or other components within or embodied on personal computers,servers or server computing devices such as routing/connectivitycomponents, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, consumer electronicdevices, network PCs, other existing computer platforms, distributedcomputing environments that include one or more of the above systems ordevices, etc.

In some instances, aspects of the system and method may be achieved viaor performed by logic and/or logic instructions including programmodules, executed in association with such components or circuitry, forexample. In general, program modules may include routines, programs,objects, components, data structures, etc. that perform particular tasksor implement particular instructions herein. The inventions may also bepracticed in the context of distributed software, computer, or circuitsettings where circuitry is connected via communication buses, circuitryor links. In distributed settings, control/instructions may occur fromboth local and remote computer storage media including memory storagedevices.

The software, circuitry and components herein may also include and/orutilize one or more type of computer readable media. Computer readablemedia can be any available media that is resident on, associable with,or can be accessed by such circuits and/or computing components. By wayof example, and not limitation, computer readable media may comprisecomputer storage media and communication media. Computer storage mediaincludes volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to store the desired information and can accessed bycomputing component. Communication media may comprise computer readableinstructions, data structures, program modules and/or other components.Further, communication media may include wired media such as a wirednetwork or direct-wired connection, however no media of any such typeherein includes transitory media. Combinations of the any of the aboveare also included within the scope of computer readable media.

In the present description, the terms component, module, device, etc.may refer to any type of logical or functional software elements,circuits, blocks and/or processes that may be implemented in a varietyof ways. For example, the functions of various circuits and/or blockscan be combined with one another into any other number of modules. Eachmodule may even be implemented as a software program stored on atangible memory (e.g., random access memory, read only memory, CD-ROMmemory, hard disk drive, etc.) to be read by a central processing unitto implement the functions of the innovations herein. Or, the modulescan comprise programming instructions transmitted to a general purposecomputer or to processing/graphics hardware via a transmission carrierwave. Also, the modules can be implemented as hardware logic circuitryimplementing the functions encompassed by the innovations herein.Finally, the modules can be implemented using special purposeinstructions (SIMD instructions), field programmable logic arrays or anymix thereof which provides the desired level performance and cost.

As disclosed herein, features consistent with the disclosure may beimplemented via computer-hardware, software and/or firmware. Forexample, the systems and methods disclosed herein may be embodied invarious forms including, for example, a data processor, such as acomputer that also includes a database, digital electronic circuitry,firmware, software, or in combinations of them. Further, while some ofthe disclosed implementations describe specific hardware components,systems and methods consistent with the innovations herein may beimplemented with any combination of hardware, software and/or firmware.Moreover, the above-noted features and other aspects and principles ofthe innovations herein may be implemented in various environments. Suchenvironments and related applications may be specially constructed forperforming the various routines, processes and/or operations accordingto the invention or they may include a general-purpose computer orcomputing platform selectively activated or reconfigured by code toprovide the necessary functionality. The processes disclosed herein arenot inherently related to any particular computer, network,architecture, environment, or other apparatus, and may be implemented bya suitable combination of hardware, software, and/or firmware. Forexample, various general-purpose machines may be used with programswritten in accordance with teachings of the invention, or it may be moreconvenient to construct a specialized apparatus or system to perform therequired methods and techniques.

Aspects of the method and system described herein, such as the logic,may also be implemented as functionality programmed into any of avariety of circuitry, including programmable logic devices (“PLDs”),such as field programmable gate arrays (“FPGAs”), programmable arraylogic (“PAL”) devices, electrically programmable logic and memorydevices and standard cell-based devices, as well as application specificintegrated circuits. Some other possibilities for implementing aspectsinclude: memory devices, microcontrollers with memory (such as EEPROM),embedded microprocessors, firmware, software, etc. Furthermore, aspectsmay be embodied in microprocessors having software-based circuitemulation, discrete logic (sequential and combinatorial), customdevices, fuzzy (neural) logic, quantum devices, and hybrids of any ofthe above device types. The underlying device technologies may beprovided in a variety of component types, e.g., metal-oxidesemiconductor field-effect transistor (“MOSFET”) technologies likecomplementary metal-oxide semiconductor (“CMOS”), bipolar technologieslike emitter-coupled logic (“ECL”), polymer technologies (e.g.,silicon-conjugated polymer and metal-conjugated polymer-metalstructures), mixed analog and digital, and so on.

It should also be noted that the various logic and/or functionsdisclosed herein may be enabled using any number of combinations ofhardware, firmware, and/or as data and/or instructions embodied invarious machine-readable or computer-readable media, in terms of theirbehavioral, register transfer, logic component, and/or othercharacteristics. Computer-readable media in which such formatted dataand/or instructions may be embodied include, but are not limited to,non-volatile storage media in various forms (e.g., optical, magnetic orsemiconductor storage media) though again does not include transitorymedia. Unless the context clearly requires otherwise, throughout thedescription, the words “comprise,” “comprising,” and the like are to beconstrued in an inclusive sense as opposed to an exclusive or exhaustivesense; that is to say, in a sense of “including, but not limited to.”Words using the singular or plural number also include the plural orsingular number respectively. Additionally, the words “herein,”“hereunder,” “above,” “below,” and words of similar import refer to thisapplication as a whole and not to any particular portions of thisapplication. When the word “or” is used in reference to a list of two ormore items, that word covers all of the following interpretations of theword: any of the items in the list, all of the items in the list and anycombination of the items in the list.

Although certain presently preferred implementations of the inventionhave been specifically described herein, it will be apparent to thoseskilled in the art to which the invention pertains that variations andmodifications of the various implementations shown and described hereinmay be made without departing from the spirit and scope of theinvention. Accordingly, it is intended that the invention be limitedonly to the extent required by the applicable rules of law.

While the foregoing has been with reference to a particular embodimentof the disclosure, it will be appreciated by those skilled in the artthat changes in this embodiment may be made without departing from theprinciples and spirit of the disclosure, the scope of which is definedby the appended claims.

1. A system, comprising: a computer system having a processor and amemory; the processor configured to receive data about a health servicetransaction; and the processor configured to generate a risk measure forthe health service transaction using the data about the health servicetransaction.
 2. The system of 1, wherein the processor is configured touse a probabilistic graph model to generate the risk measure.
 3. Thesystem of claim 2, wherein the processor is configured to generate apayor bayes graph, a provider bayes graph and a consumer bayes graph. 4.The system of claim 3, wherein the processor is configured to generatethe risk measure by combining the payor bayes graph, the provider bayesgraph and the consumer bayes graph.
 5. The system of claim 2, whereinthe processor is configured to use a stepwise belief network to generatethe probabilistic graph model.
 6. The system of claim 1, wherein theprocessor is configured to generate a risk score for an entity involvedin the health service transaction.
 7. The system of claim 6, wherein theentity is one of a health service consumer, a health service providerand a health service payer.
 8. The system of claim 1, wherein the riskmeasure is a risk score.
 9. A method, comprising: receiving data about ahealth service transaction; and generating a risk measure for the healthservice transaction using the data about the health service transaction.10. The method of 9, wherein generating the risk measure furthercomprises using a probabilistic graph model to generate the riskmeasure.
 11. The method of claim 10, wherein using the probabilisticgraph model further comprises generating a payor bayes graph, generatinga provider bayes graph and generating a consumer bayes graph.
 12. Themethod of claim 11, wherein generating the risk measure furthercomprises combining the payor bayes graph, the provider bayes graph andthe consumer bayes graph to generate the risk measure.
 13. The method ofclaim 10, wherein using the probabilistic graph model further comprisesusing a stepwise belief network to generate the probabilistic graphmodel.
 14. The method of claim 9 further comprising generating a riskscore for an entity involved in the health service transaction.
 15. Themethod of claim 14, wherein the entity is one of a health serviceconsumer, a health service provider and a health service payer.
 16. Themethod of claim 9, wherein the risk measure is a risk score.
 17. Anapparatus, comprising: a processor; a memory; the processor configuredto receive data about a health service transaction; and the processorconfigured to generate a risk measure for the health service transactionusing the data about the health service transaction.
 18. The apparatusof 17, wherein the processor is configured to use a probabilistic graphmodel to generate the risk measure.
 19. The apparatus of claim 18,wherein the processor is configured to generate a payor bayes graph, aprovider bayes graph and a consumer bayes graph.
 20. The apparatus ofclaim 19, wherein the processor is configured to generate the riskmeasure by combining the payor bayes graph, the provider bayes graph andthe consumer bayes graph.
 21. The apparatus of claim 18, wherein theprocessor is configured to use a stepwise belief network to generate theprobabilistic graph model.
 22. The apparatus of claim 17, wherein theprocessor is configured to generate a risk score for an entity involvedin the health service transaction.
 23. The apparatus of claim 22,wherein the entity is one of a health service consumer, a health serviceprovider and a health service payer.
 24. The apparatus of claim 17,wherein the risk measure is a risk score.